Method, device and equipment for determining critical ice shape of two-dimensional airfoil and storage medium

Abstract

The application provides a two-dimensional airfoil critical ice shape determination method, device and equipment and a storage medium, relates to the airfoil icing data processing technical field, and the method can more accurately describe the critical influence of the airfoil aerodynamic performance by determining the evaluation parameter corresponding to each icing condition in the current population, performs genetic operation on the current population to obtain a new current population, and determines the evaluation parameter corresponding to each icing condition in the current population again, determines a target icing condition based on the maximum value of the evaluation parameters corresponding to all icing conditions, and determines a two-dimensional airfoil critical ice shape based on the ice shape characteristic parameter corresponding to the target icing condition. By using the evaluation parameter and the genetic algorithm, the global optimal critical ice shape can be automatically found in the entire continuous flow field parameter set space, the locality and blindness of the manual point selection method are overcome, and the efficiency and accuracy of the critical ice shape determination can be improved.

Description

Technical Field

[0001] This application relates to the field of airfoil icing data processing technology, specifically to a two-dimensional airfoil critical icing shape determination method, apparatus, equipment, and storage medium. Background Technology

[0002] When an aircraft encounters supercooled water droplets in clouds during flight, icing can occur. If icing forms on the aircraft's lifting surfaces, it disrupts the original aerodynamic shape, affecting the aircraft's handling and stability, leading to decreased lift, increased drag, and even threatening flight safety. Currently, methods for verifying airworthiness compliance under icing conditions mainly include flight tests under natural icing conditions, dry air flight tests with artificial critical icing shapes, icing wind tunnel tests, and computational analysis. Because icing flight tests are high-risk and costly, multiple simulated icing tests with artificial critical icing shapes are typically conducted before natural icing flight tests. This reduces the number of natural icing flight tests, significantly lowering costs and improving efficiency. Therefore, the prediction and design of critical icing shapes are crucial for aircraft airworthiness certification.

[0003] The critical ice shape determination method in related technologies involves manually selecting a small number of icing state points, then using icing calculation software and combining it with icing parameter sensitivity analysis to preliminarily determine the critical ice shape. However, the methods in related technologies can only select the most stringent icing conditions from existing icing conditions and cannot determine the optimal critical ice shape based on all icing conditions. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for determining the critical icing shape of a two-dimensional airfoil, which can screen out the critical icing shape of a two-dimensional airfoil that is more consistent with the actual situation from a large number of icing conditions.

[0005] This application provides a method for determining the critical icing shape of a two-dimensional airfoil, including:

[0006] Obtain the flow field parameter set, which includes multiple flow field parameters, namely liquid water content, average volume diameter, flight speed, flight altitude, angle of attack, and atmospheric static temperature. Each flow field parameter corresponds to a parameter range.

[0007] An initial population is determined based on a set of flow field parameters, and the initial population is then identified as the current population. The initial population includes multiple icing conditions, and each icing condition includes a value of a flow field parameter in the set of flow field parameters.

[0008] The freezing evaluation process includes: performing freezing calculations for each freezing condition in the current population to determine the ice shape corresponding to each freezing condition; and determining the evaluation parameters corresponding to each freezing condition based on the ice shape corresponding to each freezing condition.

[0009] The iterative steps include: performing genetic operations on the current population to obtain a new current population, and performing an icing evaluation step;

[0010] Repeat the iterative steps until the preset maximum number of iterations is reached. Determine the target icing condition based on the maximum value of the evaluation parameters, and determine the critical ice type of the two-dimensional airfoil based on the ice type characteristic parameters corresponding to the target icing condition.

[0011] Optionally, icing calculations are performed for each icing condition in the current population to determine the ice accumulation type corresponding to each icing condition, including:

[0012] The velocity distribution is determined based on the incoming flow velocity, incoming flow temperature, incoming flow angle of attack, and flight altitude.

[0013] Based on the liquid water content and droplet volume diameter, the flow tube method is used to determine the droplet collection coefficient of the airfoil surface;

[0014] Phase transition calculations were performed based on the water droplet collection coefficient and velocity distribution to obtain the ice thickness distribution;

[0015] The ice accumulation pattern is obtained based on the ice thickness distribution.

[0016] Optionally, the evaluation parameters include a first projection height, a second projection height, a dimensionless relative ice layer thickness, and weighted parameters. Based on the ice accumulation type corresponding to each icing condition, the evaluation parameters corresponding to each icing condition are determined, including:

[0017] The ice shape parameters are determined based on the ice accumulation pattern. The ice shape parameters include the height of the upper ice corner, the angle of the upper ice corner, the height of the lower ice corner, the angle of the lower ice corner, and the ice area.

[0018] The first projection height is determined based on the height and angle of the upper ice angle. The first projection height represents the dimensionless projection height of the upper ice angle in the lift direction.

[0019] The second projection height is determined based on the lower ice angle height and the lower ice angle angle. The second projection height characterizes the dimensionless projection height of the lower ice angle in the lift direction.

[0020] Determine the relative thickness of the dimensionless ice layer based on the icing area;

[0021] The first projection height, the second projection height, and the dimensionless relative thickness of the ice layer are weighted to determine the weighting parameters.

[0022] Optionally, the weighting value corresponding to the first projection height is 0.4, the weighting value corresponding to the second projection height is 0.4, and the weighting value corresponding to the dimensionless relative thickness of the ice layer is 0.2.

[0023] Optionally, the target icing condition is determined based on the maximum value of the evaluation parameters, including:

[0024] The icing condition corresponding to the maximum first projection height is determined as the first candidate condition.

[0025] The icing condition corresponding to the maximum second projection height is determined as the second candidate condition.

[0026] The freezing condition corresponding to the maximum relative thickness of the dimensionless ice layer was determined as the third candidate condition.

[0027] The icing condition corresponding to the maximum weighted parameter value is determined as the fourth candidate condition.

[0028] Based on the first candidate working condition, the second candidate working condition, the third candidate working condition, and the fourth candidate working condition, the target icing working condition is determined.

[0029] Optionally, based on the first candidate working condition, the second candidate working condition, the third candidate working condition, and the fourth candidate working condition, a target icing working condition is determined, including:

[0030] Aerodynamic verification was performed on the first, second, third, and fourth candidate working conditions, and the verification results for each candidate working condition were obtained.

[0031] Based on the verification results corresponding to each candidate working condition, the target icing working condition is determined.

[0032] Optionally, the initial population is determined based on the set of flow field parameters, including:

[0033] The Latin hypercube sampling method was used to determine multiple icing conditions in the flow field parameters, which were then used as the initial population for the genetic algorithm.

[0034] To achieve the above and other related objectives, this application provides a two-dimensional airfoil critical icing shape determination device, comprising:

[0035] The data acquisition module is used to acquire the flow field parameter set, which includes multiple flow field parameters, namely liquid water content, average volume diameter, flight speed, flight altitude, angle of attack, and atmospheric static temperature. Each flow field parameter corresponds to a parameter range.

[0036] The population determination module is used to determine an initial population based on a set of flow field parameters and to determine the initial population as the current population. The initial population includes multiple icing conditions, and each icing condition includes a value of a flow field parameter in the set of flow field parameters.

[0037] The first execution module is used to execute the icing evaluation step, which includes: performing icing calculations for each icing condition in the current population, determining the post-icing flow field grid corresponding to each icing condition; and determining the evaluation parameters corresponding to each icing condition based on the icing shape corresponding to each icing condition.

[0038] The second execution module is used to execute the iteration steps, which include: performing genetic operations on the current population to obtain a new current population, and performing the freezing evaluation step;

[0039] The target determination module is used to repeatedly execute the iterative steps until the preset maximum number of iterations is reached. It determines the target icing condition based on the maximum value of the evaluation parameters, and determines the critical ice type of the two-dimensional airfoil based on the ice type characteristic parameters corresponding to the target icing condition.

[0040] To achieve the above and other related objectives, this application also provides an electronic device, including a memory and a processor, wherein the processor is configured to execute program instructions stored in the memory to implement one or more of the aforementioned methods for determining the critical ice shape of a two-dimensional airfoil.

[0041] To achieve the above and other related objectives, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform one or more of the aforementioned methods for determining the critical ice shape of a two-dimensional airfoil.

[0042] As described above, the method, apparatus, device, and storage medium for determining the critical icing shape of a two-dimensional airfoil provided in this application have the following beneficial effects:

[0043] This application discloses a method for determining the critical icing shape of a two-dimensional airfoil. The method first determines an initial population including multiple icing conditions based on a set of flow field parameters, and then designates this initial population as the current population. It then determines the evaluation parameters corresponding to each icing condition within the current population. These evaluation parameters can accurately describe the critical impact on the airfoil's aerodynamic performance. A genetic operation is performed on the current population to obtain a new current population, and the evaluation parameters corresponding to each icing condition within this current population are determined again. This iterative process is repeated until a preset maximum number of iterations is reached. Finally, the target icing condition is determined based on the maximum value of the evaluation parameters corresponding to all icing conditions, and the critical icing shape of the two-dimensional airfoil is determined based on the icing shape characteristic parameters corresponding to the target icing condition. By employing evaluation parameters and a genetic algorithm, the globally optimal critical icing shape can be automatically found within the entire continuous flow field parameter set space, overcoming the locality and blindness of manual point selection methods, and improving the efficiency and accuracy of critical icing shape determination.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0046] Figure 1 This is a flowchart illustrating a two-dimensional airfoil critical ice shape determination method, as shown in an exemplary embodiment of this application.

[0047] Figure 2 This is a schematic diagram illustrating ice-type characteristic parameters in an exemplary embodiment of this application;

[0048] Figure 3 This is a structural block diagram of a two-dimensional airfoil critical ice shape determination device, as illustrated in an exemplary embodiment of this application. Detailed Implementation

[0049] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0051] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0052] Please see Figure 1 , Figure 1 This is a flowchart illustrating a two-dimensional airfoil critical icing shape determination method, as shown in an exemplary embodiment of this application. (Reference) Figure 1It can be seen that the method for determining the critical icing shape of a two-dimensional airfoil may include:

[0053] Step S110: Obtain the set of flow field parameters.

[0054] The flow field parameter set includes multiple flow field parameters, namely liquid water content, average volume diameter, flight speed, flight altitude, angle of attack, and atmospheric static temperature. Each flow field parameter corresponds to a parameter range.

[0055] In one embodiment of this application, a set of flow field parameters can be obtained. The parameter ranges of all flow field parameters can form an icing envelope, which is a boundary constraint map defining the range and severity of atmospheric icing conditions that an aircraft may encounter during flight.

[0056] For example, the parameters for flight altitude can range from 0 to 6700 m, flight speed from 60 to 140 m / s, angle of attack from 0 to 6°, atmospheric static temperature from -30 to 0°, average volume diameter from 15 to 40 μm, and liquid water content can be determined based on atmospheric static temperature and average volume diameter.

[0057] Step S120: Determine the initial population based on the set of flow field parameters, and then define the initial population as the current population.

[0058] The initial population includes multiple icing conditions, and each icing condition includes a value of one of the flow field parameters in the flow field parameter set.

[0059] In one embodiment of this application, an initial population can be determined based on a set of flow field parameters, and this initial population can be designated as the current population. A value can be selected from the parameter ranges corresponding to each flow field parameter to form an icing condition. The icing conditions differ within the current population, and also differ between different populations. The initial population is the first set of candidate solutions for the genetic algorithm to begin searching the solution space. It serves as the gene pool for the algorithm's exploration of the solution space, determining the starting point for subsequent genetic operations, which may include selection, crossover, and mutation.

[0060] Optionally, the process of determining the initial population based on the set of flow field parameters may include: using the Latin hypercube sampling method to determine multiple icing conditions in the flow field parameters as the initial population for the genetic algorithm.

[0061] Step S130: Perform the icing evaluation step, which includes: performing icing calculations for each icing condition in the current population to determine the ice shape corresponding to each icing condition; and determining the evaluation parameters corresponding to each icing condition based on the ice shape corresponding to each icing condition.

[0062] In one embodiment of this application, an icing evaluation step may be performed, which may include: performing icing calculations for each icing condition in the current population to determine the ice shape corresponding to each icing condition; and determining the evaluation parameters corresponding to each icing condition based on the ice shape corresponding to each icing condition.

[0063] Optionally, icing calculations are performed for each icing condition in the current population to determine the ice shape corresponding to each icing condition, including: determining the velocity distribution based on the incoming flow velocity, incoming flow temperature, incoming flow angle of attack, and flight altitude; determining the water droplet collection coefficient on the airfoil surface using the flow tube method based on the liquid water content and water droplet volume diameter; performing phase transition calculations based on the water droplet collection coefficient and velocity distribution to obtain the ice thickness distribution; and obtaining the ice shape based on the ice thickness distribution.

[0064] For example, the expression for determining the velocity distribution may include:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] in, For the incoming flow velocity, For the angle of attack of the incoming flow, Spatial geometric coordinates, The dipole strength of the airfoil surface. The point source intensity of the airfoil surface. This represents the distance and angle from the spatial geometric location to the current computational unit. The thickness of the boundary layer is the momentum loss layer. For shape factor, For the Mach number of the incoming flow, The coefficient of friction, The drag coefficient, For velocity distribution, Energy thickness shape factor, δ* represents the density-thickness shape factor; δ* represents the boundary layer displacement thickness; δ represents the power supply intensity; the subscript i represents the index; s represents the arc length.

[0070] Solving the simultaneous equations for the velocity distribution yields the air velocity field around the two-dimensional airfoil, i.e., the velocity distribution. .

[0071] For example, when determining the water droplet collection coefficient using the flow tube method, individual water droplets must be tracked to determine whether they collide with the object. For regular water droplets, some simplifying assumptions can be made during the calculation; for supercooled large water droplets, the dynamic effects are analyzed separately. The assumptions are as follows:

[0072] (1) Water droplets neither condense nor decompose during their movement, and their volume remains constant, but their shape can change.

[0073] (2) The temperature and density of the water droplets remain constant throughout the process.

[0074] (3) The initial velocity of the water droplet is equal to the velocity of the free flow. The volume of the water droplet occupies a very small proportion of the space. The airflow does not affect the flow field of the airflow over the surface of the object due to the presence of supercooled water droplets.

[0075] (4) The forces acting on the water droplet are only air resistance, the weight of the water droplet and the buoyancy of the air.

[0076] Based on the forces acting on the water droplet, the trajectory of a single water droplet under the Lagrange method is established:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] in, For the speed of the water droplets, Let be the velocity of the airflow at the location of the water droplet. air density, For the density of water droplets, It is the acceleration due to gravity. It is the aerodynamic coefficient. The diameter of the water droplet is [missing information]. The coefficient of water droplet resistance. The relative Reynolds number is defined based on relative velocity and droplet size. The equivalent particle diameter, It has air viscosity.

[0082] A complete droplet trajectory can be obtained by calculating the trajectory of a single droplet. Then, the limiting trajectory and flow tube can be determined. The limiting trajectory refers to the trajectory of droplets that just graz the outermost edge of the airfoil surface; it defines the boundary between droplets that will collide with the airfoil and those that will not. The flow tube is a tubular region enclosed by two adjacent limiting trajectories, essentially a "passageway" for a cluster of droplets. By calculating numerous droplet trajectories with different initial positions, key limiting trajectories are found, thus defining at least one flow tube.

[0083] After a water droplet impacts a wall surface, the impact characteristics of the droplet on the wall element are typically characterized by the collection coefficient. The flow tube method derives the local collection coefficient based on the definition of the water droplet collection coefficient:

[0084] ;

[0085] in, Let be the water droplet collection coefficient. Represents the tiny units on the surface of an airfoil. This represents the width of the impact zone when the flow tube strikes a point on the airfoil surface. Water droplet collection coefficient. The amount of water impacted per unit area at each location on the airfoil surface.

[0086] For example, the ice thickness distribution can be obtained by performing phase transition calculations based on the water droplet collection coefficient and velocity distribution. The phase transition calculations primarily employ the Messier freezing phase transition calculation model.

[0087] Based on the conservation conditions, the governing equations of the Messier model can be obtained as follows:

[0088] ;

[0089] ;

[0090] in, For the mass of collected liquid water, The mass of liquid water flowing in from the previous unit. For evaporation quality, To determine the current mass of liquid water flowing out of the unit, To freeze the liquid water mass, For the collected energy, The energy flowing in from the previous unit, Heating of the surface by air friction. Energy is lost due to convective heat transfer. For evaporation energy, To allow the current unit energy to flow out, This refers to the energy of the freezing phase transition; it should be noted that once the current unit is determined, its adjacent unit is either the previous unit or the next unit.

[0091] ;

[0092] The expressions for each energy term are as follows:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] in, The convective heat transfer coefficient is... To control body surface area, The surface temperature of the object. The temperature of the incoming airflow. The fluid restitution coefficient, For the free flow velocity, For the specific heat of air, Latent heat of vaporization For the specific heat of water, To dissolve latent heat, Indicates the mass of evaporated water. This represents the mass of the frozen water. Each heat flow term in the heat balance equation can be solved independently from relevant aerodynamic or thermodynamic equations or given by design conditions. The heat flow terms in the energy balance equation are solved sequentially. Convective heat transfer coefficient. Closely related to the boundary layer state, the boundary layer parameters (such as the surface friction coefficient) provided by the flow field calculation are usually calculated by methods such as Reynolds analogy. The air velocity field determines the development of the boundary layer, and thus determines the heat transfer intensity.

[0099] For example, the process of obtaining the flow field mesh after ice accumulation based on the ice thickness distribution may include:

[0100] Mesh generation calculations primarily utilize quaternion interpolation-Laplacian smooth coupling technology to automatically generate the flow field mesh after ice accumulation. First, the deformation of the wall nodes is decomposed into rotation and stretching, both expressed using quaternions. Then, based on an exponential mapping interpolation method in Lie algebra space, the wall deformation is interpolated to the flow field nodes, as shown below:

[0101] ;

[0102] Where x represents the coordinates of the computational grid nodes. For the weight function, It is the Euclidean distance from the point to be interpolated to the i-th wall node. The known deformation data is for the wall nodes, and n represents the number of boundary nodes. To avoid generating invalid meshes in the above process, an improved Laplace operator is used to calculate the displacement of the computational domain mesh nodes. Details are as follows:

[0103] ;

[0104] in, Represents the volume of a mesh cell. Represents the mesh surface. This represents the amount of deformation at the node. The diffusion coefficient can be calculated from the wall distance and the mesh volume. The Gauss-Sedial iterative method is used to solve the above linear equations:

[0105] ;

[0106] in, The slack coefficient represents the relaxation coefficient in the iterative calculation format, the superscript n indicates the iteration level, and f represents the source term.

[0107] Optionally, the evaluation parameters include a first projection height, a second projection height, a dimensionless relative ice thickness, and weighting parameters. Based on the post-icing flow field grid corresponding to each icing condition, the evaluation parameters for each icing condition are determined, including: determining ice shape parameters based on the post-icing flow field grid, including the upper ice corner height, upper ice corner angle, lower ice corner height, lower ice corner angle, and icing area; determining the first projection height based on the upper ice corner height and upper ice corner angle, the first projection height representing the dimensionless projection height of the upper ice corner in the lift direction; determining the second projection height based on the lower ice corner height and lower ice corner angle, the second projection height representing the dimensionless projection height of the lower ice corner in the lift direction; determining the dimensionless relative ice thickness based on the icing area; and weighting the first projection height, second projection height, and dimensionless relative ice thickness to determine the weighting parameters.

[0108] Optionally, the weighting value corresponding to the first projection height is 0.4, the weighting value corresponding to the second projection height is 0.4, and the weighting value corresponding to the dimensionless relative thickness of the ice layer is 0.2.

[0109] For example, the expression for the evaluation parameter is shown below:

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] in, The first projection height, The second projection height, is the icing area, is the weighting parameter, and c is the airfoil chord length.

[0115] For example, please refer to Figure 2 This is a schematic diagram illustrating ice-type characteristic parameters in an exemplary embodiment of this application. x / c and y / c represent dimensionless coordinates.

[0116] It should be noted that for dimensionless upper / lower ice angle heights, aerodynamic performance (such as maximum lift coefficient and stall angle of attack) is extremely sensitive to ice angle height. There is usually a "critical height" beyond which performance deteriorates drastically. Therefore, directly controlling the ice angle height is directly controlling the most critical aerodynamic risk.

[0117] A larger icing area implies greater wetted perimeter and surface friction resistance. The dimensionless icing area reflects the overall disturbance of the boundary layer by the ice layer, resulting in airflow loss.

[0118] The first three parameters (upper ice angle, lower ice angle, and icing area) may conflict during the optimization process. For example, a certain ice shape may have a very high upper ice angle but a small icing area; another may have neither the highest upper nor lower ice angles, but a large total area. The weighted parameter, through weighted summation, integrates these three interrelated and potentially conflicting objectives into a single, comprehensive indicator.

[0119] Step S140: Perform an iterative step, which includes: performing genetic operations on the current population to obtain a new current population, and performing an icing evaluation step.

[0120] In one embodiment of this application, an iterative step can be performed, which may include: performing genetic operations on the current population to obtain a new current population, and performing an icing evaluation step. The genetic operations may include selection, crossover, and mutation operations. The population represents a set of samples in the search space. The initial population is generated randomly or heuristically. Based on a population-based, iterative evolutionary process, the genetic algorithm can efficiently navigate the vast solution space and ultimately approach the global optimum.

[0121] After determining the evaluation parameters corresponding to each icing condition in the current population in step S130, a selection operation using a genetic algorithm can be performed. Parents are selected from icing conditions with higher fitness, and offspring are generated through gene crossover according to a set crossover probability. Gene perturbation is then performed on new icing conditions according to the mutation probability to form a new generation population. The terminal uses this as the new current population and repeats step S130.

[0122] Step S150: Repeat the iterative steps until the preset maximum number of iterations is reached. Determine the target icing condition based on the maximum value of the evaluation parameters, and determine the critical ice type of the two-dimensional airfoil based on the ice type characteristic parameters corresponding to the target icing condition.

[0123] In one embodiment of this application, the iterative steps can be repeated until a preset maximum number of iterations is reached. A target icing condition is determined based on the maximum value of the evaluation parameters, and the critical icing shape of the two-dimensional airfoil is determined based on the icing shape characteristic parameters corresponding to the target icing condition. The critical icing shape of a two-dimensional airfoil refers to the icing shape formed on the airfoil surface under specific icing conditions that causes the aircraft's aerodynamic performance to degrade to an acceptable safety limit.

[0124] For example, the maximum number of iterations can be preset to 50.

[0125] Optionally, determining the target icing condition based on the maximum value of the evaluation parameters includes: determining the icing condition corresponding to the maximum value of the first projection height as the first candidate condition; determining the icing condition corresponding to the maximum value of the second projection height as the second candidate condition; determining the icing condition corresponding to the maximum value of the dimensionless relative thickness of the ice layer as the third candidate condition; determining the icing condition corresponding to the maximum value of the weighted parameter as the fourth candidate condition; and determining the target icing condition based on the first candidate condition, the second candidate condition, the third candidate condition, and the fourth candidate condition.

[0126] Optionally, based on the first candidate working condition, the second candidate working condition, the third candidate working condition, and the fourth candidate working condition, the target icing working condition is determined, including: performing aerodynamic verification on the first candidate working condition, the second candidate working condition, the third candidate working condition, and the fourth candidate working condition to obtain the verification result corresponding to each candidate working condition; and determining the target icing working condition based on the verification result corresponding to each candidate working condition.

[0127] Operators can pre-set evaluation criteria, such as the maximum lift loss or the greatest increase in drag. They can perform computational fluid dynamics (CFD) simulations based on candidate operating conditions to obtain verification results, which can be either lift loss values ​​or drag increase values. The verification results and evaluation criteria can correspond. When performing computational fluid dynamics simulations, the icing characteristic parameters corresponding to candidate operating conditions can be added to a clean airfoil model to obtain an icing airfoil model. The icing airfoil model is then meshed, and simulation-related boundary conditions and other constraints are set. Finally, the lift coefficient or drag coefficient is obtained by solving the icing airfoil model and the simulation-related boundary conditions and constraints. Based on the maximum lift coefficient corresponding to the clean airfoil model and the maximum lift coefficient corresponding to each candidate operating condition, the lift loss rate corresponding to each candidate operating condition is determined. The candidate operating condition corresponding to the maximum lift loss rate is then determined as the target icing operating condition. Alternatively, based on the zero-lift drag coefficient corresponding to the clean airfoil model and the zero-lift drag coefficient corresponding to each candidate operating condition, the zero-lift drag increase rate corresponding to each candidate operating condition is determined. The candidate operating condition corresponding to the maximum zero-lift drag increase rate is then determined as the target icing operating condition. The zero-lift drag coefficient can be the drag coefficient corresponding to an angle of attack of 0°.

[0128] Figure 3 This is a block diagram illustrating a two-dimensional airfoil critical icing shape determination device, as shown in an exemplary embodiment of this application. Figure 3 As shown, the exemplary two-dimensional airfoil critical icing shape determination device 300 includes:

[0129] The data acquisition module is used to acquire the flow field parameter set, which includes multiple flow field parameters, namely liquid water content, average volume diameter, flight speed, flight altitude, angle of attack, and atmospheric static temperature. Each flow field parameter corresponds to a parameter range.

[0130] The population determination module is used to determine the initial population based on the set of flow field parameters and to determine the initial population as the current population. The initial population includes multiple icing conditions, and the icing conditions include a value of each flow field parameter in the set of flow field parameters.

[0131] The first execution module is used to execute the icing evaluation step, which includes: performing icing calculations for each icing condition in the current population, determining the post-icing flow field grid corresponding to each icing condition; and determining the evaluation parameters corresponding to each icing condition based on the post-icing flow field grid corresponding to each icing condition.

[0132] The second execution module is used to execute the iteration steps, which include: performing genetic operations on the current population to obtain a new current population, and performing the freezing evaluation step;

[0133] The target determination module is used to repeatedly execute the iterative steps until the preset maximum number of iterations is reached. It determines the target icing condition based on the maximum value of the evaluation parameters, and determines the critical ice type of the two-dimensional airfoil based on the ice type characteristic parameters corresponding to the target icing condition.

[0134] It should be noted that the two-dimensional airfoil critical icing shape determination device and the two-dimensional airfoil critical icing shape determination method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the two-dimensional airfoil critical icing shape determination device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0135] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the two-dimensional airfoil critical ice shape determination method provided in the above embodiments.

[0136] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the two-dimensional airfoil critical ice shape determination method provided in the various embodiments described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0137] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the two-dimensional airfoil critical icing shape determination method provided in the various embodiments described above.

[0138] In the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "comprising" and "including" as used throughout the specification and claims are open-ended terms and should therefore be interpreted as "comprising but not limited to".

[0139] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for determining the critical ice shape of a two-dimensional airfoil, characterized in that, The method comprises the following steps: acquiring a set of flow field parameters, the set of flow field parameters comprising a plurality of flow field parameters, respectively liquid water content, mean volume diameter, flight speed, flight height, angle of attack and atmospheric static temperature, each flow field parameter corresponding to a parameter range; determining an initial population based on the set of flow field parameters, and determining the initial population as a current population, the initial population comprising a plurality of icing conditions, each icing condition comprising a value of each flow field parameter in the set of flow field parameters; performing an icing evaluation step, the icing evaluation step comprising: performing icing calculation on each icing condition in the current population to determine an ice-accumulated flow field grid corresponding to each icing condition; and determining an evaluation parameter corresponding to each icing condition based on the ice-accumulated flow field grid corresponding to each icing condition; wherein the evaluation parameter comprises a first projected height, a second projected height, a dimensionless ice layer relative thickness and a weighted parameter, the evaluation parameter corresponding to each icing condition is determined based on an ice shape corresponding to each icing condition, comprising: determining an ice type parameter based on the ice shape, the ice type parameter comprising an upper ice horn height, an upper ice horn angle, a lower ice horn height, a lower ice horn angle and an icing area; determining the first projected height based on the upper ice horn height and the upper ice horn angle, the first projected height representing a dimensionless projected height of the upper ice horn in the lift direction; determining the second projected height based on the lower ice horn height and the lower ice horn angle, the second projected height representing a dimensionless projected height of the lower ice horn in the lift direction; determining the dimensionless ice layer relative thickness based on the icing area; weighting the first projected height, the second projected height and the dimensionless ice layer relative thickness to determine the weighted parameter; performing an iteration step, the iteration step comprising: performing genetic operation on the current population to obtain a new current population, and performing the icing evaluation step; repeating the iteration step until a preset maximum iteration number is reached, determining a target icing condition based on a maximum value of the evaluation parameter, and determining a two-dimensional airfoil critical ice shape based on an ice type characteristic parameter corresponding to the target icing condition.

2. The method of claim 1, wherein, performing icing calculation on each icing condition in the current population to determine an ice-accumulated ice shape corresponding to each icing condition, comprising: determining a velocity distribution based on the incoming flow speed, the incoming flow temperature, the incoming flow angle of attack and the flight height; determining a water droplet collection coefficient on the airfoil surface by using the stream tube method based on the liquid water content and the water droplet volume diameter; performing phase change calculation based on the water droplet collection coefficient and the velocity distribution to obtain an ice thickness distribution; obtaining the ice-accumulated ice shape based on the ice thickness distribution.

3. The method of claim 1, wherein, The weighting value corresponding to the first projected height is 0.4, the weighting value corresponding to the second projected height is 0.4, and the weighting value corresponding to the dimensionless ice layer relative thickness is 0.

2.

4. The method of claim 1, wherein, determining the target icing condition based on the maximum value of the evaluation parameter, comprising: determining the icing condition corresponding to the maximum value of the first projected height as a first candidate condition; determining the icing condition corresponding to the maximum value of the second projected height as a second candidate condition; determining the icing condition corresponding to the maximum value of the dimensionless ice layer relative thickness as a third candidate condition; determining the icing condition corresponding to the maximum value of the weighted parameter as a fourth candidate condition; determining the target icing condition based on the first candidate condition, the second candidate condition, the third candidate condition and the fourth candidate condition.

5. The method of claim 4, wherein Based on the first, second, third, and fourth candidate operating conditions, the target icing condition is determined, including: Aerodynamic verification was performed on the first candidate working condition, the second candidate working condition, the third candidate working condition, and the fourth candidate working condition, and the verification results for each candidate working condition were obtained. Based on the verification results corresponding to each candidate working condition, the target icing working condition is determined.

6. The method of claim 1, wherein, The initial population is determined based on the set of flow field parameters, including: The Latin hypercube sampling method was used to determine multiple icing conditions in the flow field parameters, which were then used as the initial population for the genetic algorithm.

7. A two-dimensional airfoil critical ice shape determination apparatus, characterized by, include: The data acquisition module is used to acquire the flow field parameter set, which includes multiple flow field parameters, namely liquid water content, average volume diameter, flight speed, flight altitude, angle of attack, and atmospheric static temperature. Each flow field parameter corresponds to a parameter range. The population determination module is used to determine an initial population based on a set of flow field parameters and to determine the initial population as the current population. The initial population includes multiple icing conditions, and each icing condition includes a value of a flow field parameter in the set of flow field parameters. The first execution module is used to execute the icing evaluation step, which includes: performing icing calculations for each icing condition in the current population, determining the ice accumulation shape corresponding to each icing condition; and determining the evaluation parameters corresponding to each icing condition based on the ice accumulation shape corresponding to each icing condition. The evaluation parameters include the first projection height, the second projection height, the dimensionless relative thickness of the ice layer, and weighted parameters. Based on the ice shape corresponding to each icing condition, the evaluation parameters for each icing condition are determined, including: Ice shape parameters are determined based on the ice accumulation and ice shape. These parameters include the height of the upper ice corner, the angle of the upper ice corner, the height of the lower ice corner, the angle of the lower ice corner, and the ice area. The first projection height is determined based on the height and angle of the upper ice angle. The first projection height represents the dimensionless projection height of the upper ice angle in the lift direction. The second projection height is determined based on the lower ice angle height and the lower ice angle angle. The second projection height characterizes the dimensionless projection height of the lower ice angle in the lift direction. Determine the relative thickness of the dimensionless ice layer based on the icing area; The first projection height, the second projection height, and the dimensionless relative thickness of the ice layer are weighted to determine the weighting parameters; The second execution module is used to execute the iteration steps, which include: performing genetic operations on the current population to obtain a new current population, and performing the freezing evaluation step; The target determination module is used to repeatedly execute the iterative steps until the preset maximum number of iterations is reached. It determines the target icing condition based on the maximum value of the evaluation parameters, and determines the critical ice type of the two-dimensional airfoil based on the ice type characteristic parameters corresponding to the target icing condition.

8. An electronic device, comprising: It includes a memory and a processor, the processor being used to execute program instructions stored in the memory to implement the two-dimensional airfoil critical ice shape determination method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the two-dimensional airfoil critical ice shape determination method according to any one of claims 1 to 6.

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